Presentation 2012-11-07
Structure Learning for Anomaly Localization
Satoshi HARA, Takashi WASHIO,
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Abstract(in English) In this paper, we propose a graphical model learning algorithm for an anomaly localization. We introduce a new regularization term that penalizes the row/column-wise difference between two precision matrices and formulate the task as a convex optimization problem. We further provide an optimization algorithm based on an alternating direction method. The validity of the proposed method is presented through a simulation using a real world data.
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Keyword(in English) Anomaly Localization / Graphical Gaussian Model / Alternating Direction Method of Multipliers
Paper # IBISML2012-36
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Conference Information
Committee IBISML
Conference Date 2012/10/31(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Structure Learning for Anomaly Localization
Sub Title (in English)
Keyword(1) Anomaly Localization
Keyword(2) Graphical Gaussian Model
Keyword(3) Alternating Direction Method of Multipliers
1st Author's Name Satoshi HARA
1st Author's Affiliation The Institute of Scientific and Industrial Research (ISIR), Osaka University()
2nd Author's Name Takashi WASHIO
2nd Author's Affiliation The Institute of Scientific and Industrial Research (ISIR), Osaka University
Date 2012-11-07
Paper # IBISML2012-36
Volume (vol) vol.112
Number (no) 279
Page pp.pp.-
#Pages 6
Date of Issue